Estimating value at risk and expected shortfall: a kalman filter approach

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dc.contributor.advisorVan Vuuren, Gary
dc.contributor.authorVan Der Lecq, Maximilian
dc.date.accessioned2025-03-31T08:07:18Z
dc.date.available2025-03-31T08:07:18Z
dc.date.issued2024
dc.date.updated2025-03-31T08:04:13Z
dc.description.abstractCalculating Value-at-Risk (VaR) to estimate the maximum loss a portfolio may incur at a given confidence level and over a specified time has undergone several adaptations, iterations, and additions since its inception in 1994. In 2013, the Basel Committee on Banking Supervision (BCBS) replaced VaR with Expected Shortfall (ES), or Conditional VaR (CVaR), as the new primary measure for banking institutions to forecast market risk and hence allocate the relevant amount of regulatory market risk capital. ES measures the probability weighted losses beyond VaR, so VaR remains a crucial step in its computation and retains its significance in estimating market risk and associated measures. A Kalman filter is used for the first time to estimate both VaR (and ES) to provide an alternative technique to existing industry methods. Modelling the volatility of asset returns as a stochastic process, the Kalman filter uses Bayesian statistics to forecast unobservable data by identifying underlying patterns required to predict future values. Back-testing results (in which the number of times VaR or ES forecasted too low a value to cover the following day's market loss is compared with the prescribed confidence level) indicate that the Kalman filter is a reliable and robust contender in the volatility framework milieu, outperforming GARCH, EWMA and equally weighted measures of volatility in both volatile and calm market conditions.
dc.identifier.apacitationVan Der Lecq, M. (2024). <i>Estimating value at risk and expected shortfall: a kalman filter approach</i>. (). University of Cape Town ,Faculty of Commerce ,School of Economics. Retrieved from http://hdl.handle.net/11427/41300en_ZA
dc.identifier.chicagocitationVan Der Lecq, Maximilian. <i>"Estimating value at risk and expected shortfall: a kalman filter approach."</i> ., University of Cape Town ,Faculty of Commerce ,School of Economics, 2024. http://hdl.handle.net/11427/41300en_ZA
dc.identifier.citationVan Der Lecq, M. 2024. Estimating value at risk and expected shortfall: a kalman filter approach. . University of Cape Town ,Faculty of Commerce ,School of Economics. http://hdl.handle.net/11427/41300en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Van Der Lecq, Maximilian AB - Calculating Value-at-Risk (VaR) to estimate the maximum loss a portfolio may incur at a given confidence level and over a specified time has undergone several adaptations, iterations, and additions since its inception in 1994. In 2013, the Basel Committee on Banking Supervision (BCBS) replaced VaR with Expected Shortfall (ES), or Conditional VaR (CVaR), as the new primary measure for banking institutions to forecast market risk and hence allocate the relevant amount of regulatory market risk capital. ES measures the probability weighted losses beyond VaR, so VaR remains a crucial step in its computation and retains its significance in estimating market risk and associated measures. A Kalman filter is used for the first time to estimate both VaR (and ES) to provide an alternative technique to existing industry methods. Modelling the volatility of asset returns as a stochastic process, the Kalman filter uses Bayesian statistics to forecast unobservable data by identifying underlying patterns required to predict future values. Back-testing results (in which the number of times VaR or ES forecasted too low a value to cover the following day's market loss is compared with the prescribed confidence level) indicate that the Kalman filter is a reliable and robust contender in the volatility framework milieu, outperforming GARCH, EWMA and equally weighted measures of volatility in both volatile and calm market conditions. DA - 2024 DB - OpenUCT DP - University of Cape Town KW - Kalman Filter, Value-at-Risk LK - https://open.uct.ac.za PB - University of Cape Town PY - 2024 T1 - Estimating value at risk and expected shortfall: a kalman filter approach TI - Estimating value at risk and expected shortfall: a kalman filter approach UR - http://hdl.handle.net/11427/41300 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/41300
dc.identifier.vancouvercitationVan Der Lecq M. Estimating value at risk and expected shortfall: a kalman filter approach. []. University of Cape Town ,Faculty of Commerce ,School of Economics, 2024 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/41300en_ZA
dc.language.isoen
dc.language.rfc3066ENG
dc.publisher.departmentSchool of Economics
dc.publisher.facultyFaculty of Commerce
dc.publisher.institutionUniversity of Cape Town
dc.subjectKalman Filter, Value-at-Risk
dc.titleEstimating value at risk and expected shortfall: a kalman filter approach
dc.typeThesis / Dissertation
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMPhil
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